Student Preferences and Expectations: Some Practical Tips for Designers of English Enhancement Programmes
Bibliographic record
Abstract
As one of the essential skills for success in work and studies, English communication is often made a key component in the GE curriculum of tertiary study programmes. In addition to the provision of required English proficiency courses, many tertiary institutions have established English centres of some description to promote English learning on campus. Yet from students’ perspective, what kinds of programmes and activities should be offered, and how they feel about these initiatives is not very widely discussed in the existing literature.This paper aims to address these questions with practical experiences gained from a project to establish an English language enhancement centre by one self-financing tertiary institution in Hong Kong. Funded by the Quality Enhancement Support Scheme (QESS), the centre, titled the Centre for Academic and Professional Language Enhancement (CAPLE), was set up in March 2016. Cognizant of the benefits of life-wide learning, independent learning and collaborative peer learning, CAPLE offers a series of workshops and various kinds of English and culture learning activities that adopt these pedagogical approaches.Centre usage figures and user feedback were continuously collected for evaluative purposes. Data collected through student diaries, online surveys, usage records of the online programmes and other resources and a focus group interview showed varying levels of popularity of different types of workshops/activities, suggesting there is a need for programme designers to have a more realistic view about students’ actual English learning needs and learning motivation. Recommendations are made accordingly for more economical use of resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.051 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".